Multiple testing with structure and exploration
Abstract/Contents
- Abstract
- The last couple decades have seen the proliferation of high-throughput biological assays as well as the use of electronic medical records for research purposes. For example, the UK Biobank data set contains both genotype and electronic medical record data for half a million individuals. Such data give scientists an unprecedented ability to probe large numbers of scientific hypotheses simultaneously. The statistical field of multiple testing addresses the question of how to sort through all these potential associations without making too many false discoveries. However, modern data sets create new multiple testing challenges for which existing tools are insufficient. In this thesis, we explore some such challenges in the context of two themes: structure and exploration. While many traditional multiple testing methods implicitly treat their hypotheses exchangeably, structure is ubiquitous in hypotheses of interest for biomedical applications. These structures should be accounted for by multiple testing procedures for better power and interpretability. Additionally, most traditional testing procedures formally prohibit exploration of the data, as this would invalidate their inferential guarantees. On the other hand, the analysis of scientific data often involves at least some exploration, which leads to the need for procedures that can provide inferential guarantees while accommodating exploration. In this dissertation, we elaborate on these new challenges, review some existing work to address them, and present new proposals towards this end.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Katsevich, Eugene |
|---|---|
| Degree supervisor | Sabatti, Chiara |
| Thesis advisor | Sabatti, Chiara |
| Thesis advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Thesis advisor | Montanari, Andrea |
| Degree committee member | Candès, Emmanuel J. (Emmanuel Jean) |
| Degree committee member | Montanari, Andrea |
| Associated with | Stanford University, Department of Statistics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Eugene Katsevich. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Eugene Katsevich
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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